Dennis Ruzeski
Head of Cloud and Devops
Atlanta, Georgia, United States
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Buffalo, NY native who started in tech writing basic programs on an old TI-994a computer. This branched into a technology career doing tech support for Informix then moving into batch processing, Unix admin, Linux engineer, devops, and eventually landing in the cloud architecture space. While not working on technology, Dennis loves to cook and spend time with his wife and 2 rescue mutts.
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From Concept to Platform: Building Hivemindd with AI at the Core
What if AI could not only support development but actually help bring an entire platform from concept to delivery? In this session, we’ll explore the journey of building Hivemindd, a resourcing platform connecting startups and SMBs with fractional experts. From day one, AI wasn’t just a feature, it was a team member.
We’ll walk through how we used AI to:
Translate early ideas into functional requirements and user stories.
Accelerate technical design, architecture, and decision-making.
Support engineering teams with code scaffolding, testing, and documentation.
Drive go-to-market content, investor storytelling, and community engagement.
Along the way, we’ll share what worked, what didn’t, and how AI shifted the way we think about product development. Whether you’re curious about using AI in your own projects or leading teams through the uncertainty of new product creation, you’ll leave with practical strategies and a real-world case study of AI helping a startup move at startup speed.
AI and Your Org Chart: Bridging the AI Knowledge Gaps in Your Organization
The biggest barrier to AI adoption isn’t technology, it’s the knowledge gap between what your people know about AI and what they need to know, and that gap looks radically different depending on where someone sits in your org chart.
In this session, we’ll dismantle the myth that AI readiness is a one-size-fits-all training problem. Instead, we’ll map the specific knowledge gaps at each organizational level, from C-suite executives who can’t evaluate AI ROI, to VPs who struggle to translate executive vision into feasible AI projects, to middle managers who need to redesign workflows without breaking them, to individual contributors who must learn to critically evaluate AI outputs rather than blindly trust them.
Through real-world examples. including a $2M chatbot that nobody needed and a lightweight governance framework for a 30-person startup, attendees will learn a practical approach to bridging these gaps. The session introduces the AI Knowledge Gap Matrix, a tool for mapping existing AI literacy across organizational tiers, and a right-sized GRC (Governance, Risk & Compliance) framework designed for startups and scaling organizations that need structure without bureaucracy.
Attendees will leave with a concrete 90-day action plan for assessing knowledge gaps, building role-specific training programs, and deploying lightweight AI governance- all designed to work whether you’re a 30-person startup or a 300-person growth-stage company.
Conway’s Law and Digital Transformation
Conway’s Law posits that the design of a system mirrors the communication structure of the organization that created it. As businesses increasingly adopt modern technologies to enhance operations and deliver value, the alignment between organizational structures and system architectures becomes critical. Let's explore the implications of Conway’s Law in the context of digital transformation, examining how traditional, hierarchical structures often hinder the agility and innovation required for modern, scalable systems.
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